Design and development of new products is a challenging task. In particular, time to market and quality requirements put a lot of pressure on the engineer to design and develop new products quickly without jeopardizing the quality. Thereby, it is very important to predict the physical properties of the final product. For example, the final product may be integrated in a larger structure which often requires the product to only have specific physical properties to meet the requirements of the end manufacturer. This is for example the case in the automotive industry, in which the automotive supplier has to supply products to a car manufacturer which meet certain standards.
Several ways are known for assisting in the design and development of new products. For example, it is possible to create miniature models made out of wood or plastics to estimate the physical properties and behavior of the end product. This process is usually cumbersome and expensive. Alternatively, computer simulations to give a realistic prediction of the behavior of the product under certain conditions are gaining more and more attention and are already used today in several disciplines in mechanical, electrical and photonic engineering.
For example, the design of an automotive interior plays a pivotal role in creating a high quality, functional and appealing car, wherein designing a safe and robust interior within the guidelines of regulation agencies and car manufacturers is a challenge. Conventionally Finite Element Analysis (FEA) tools are used to validate the design before it goes into production by computer simulation.
In the Finite Element Method (FEM) a physical structure is modelled by a set of appropriate finite elements interconnected with nodes. Elements may have physical properties such as thickness, coefficient of thermal expansion, density, Young's modulus, shear modulus and Poisson's ratio.
FEM is used for finding approximate solutions of partial differential equations as well as of integral equations such as the heat transport equation. The challenge is to create an equation that approximates the equation to be studied, but is numerically stable so that errors in input data do not lead to a meaningless output.
FEA assists in obtaining a realistic prediction of stiffness and strength and also minimizing weight and materials. It helps in identifying of where structures bend or twist and indicates the distribution of stresses, strain and displacement. Elements are bounded by nodes and define localized mass, stiffness and other properties. Therefore, the product behavior can be predicted in advance so that an accurate prototype can be built.
Although FEA tools speed up the design and development process tremendously in comparison to creating several real models made of wood, plastic, metal, etc. and analyzing their physical properties, there are several problems involved with FEA.
For example, the more realistic a model has to be, the more calculation and computer time is needed to obtain information about the physical properties and behavior of the product to be modelled. Additionally, in the automotive industry, for example, there are several aspects to be analyzed such as the static properties, dynamic properties and noise, vibration and harshness (NVH) behavior.
Conventionally different FEA programs, such as Nastran, Abaqus and LS-DYNA, are used for analyzing different aspects, which increases complexity, time, costs and manpower when a product model is to be analyzed. Further, usually CAD data representing a design is used as input and has to be modified and prepared to suit the FEA program. This is a complex task, since different programs require different input data and the quality of the simulation results is varying with the input data. Accordingly, as discussed above, the modification and preparation of a model for FEA usually has to take into account the kind of analysis, such as static, dynamic or NVH, so that conventionally several different models used together with different programs are needed.
For example, a first FEA program possesses a well established code for dynamic simulations. It has good material models, contact algorithms and multi CPU scalability, however, it is not well suited for NVH and static analysis. To do NVH and static simulations, the model has to be converted to a second or third program format.